6 papers · 1 filter
Towards Autonomous Mechanistic Reasoning in Virtual Cells
Yunhui Jang, Lu Zhu, Jake Fawkes +3
Large language models (LLMs) have recently gained significant attention as a promising approach to accelerate scientific discovery. However, their application in open-ended scienti…
Virtual Cells: Predict, Explain, Discover
Emmanuel Noutahi, Jason Hartford, Prudencio Tossou +12
Drug discovery is fundamentally a process of inferring the effects of treatments on patients, and would therefore benefit immensely from computational models that can reliably simu…
A Cross Modal Knowledge Distillation & Data Augmentation Recipe for Improving Transcriptomics Representations through Morphological Features
Ihab Bendidi, Yassir El Mesbahi, Alisandra K. Denton +4
Understanding cellular responses to stimuli is crucial for biological discovery and drug development. Transcriptomics provides interpretable, gene-level insights, while microscopy…
TxPert: Leveraging Biochemical Relationships for Out-of-Distribution Transcriptomic Perturbation Prediction
Frederik Wenkel, Wilson Tu, Cassandra Masschelein +12
Accurately predicting cellular responses to genetic perturbations is essential for understanding disease mechanisms and designing effective therapies. Yet exhaustively exploring th…
Benchmarking Transcriptomics Foundation Models for Perturbation Analysis : one PCA still rules them all
Ihab Bendidi, Shawn Whitfield, Kian Kenyon-Dean +4
Understanding the relationships among genes, compounds, and their interactions in living organisms remains limited due to technological constraints and the complexity of biological…
Automated Discovery of Pairwise Interactions from Unstructured Data
Zuheng, Xu, Moksh Jain +5
Pairwise interactions between perturbations to a system can provide evidence for the causal dependencies of the underlying underlying mechanisms of a system. When observations are…